{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from __future__ import print_function\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    597816.0\n",
       "1    583104.0\n",
       "2    572465.0\n",
       "3    561279.0\n",
       "4    551589.0\n",
       "Name: number, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "discfile = '..\\\\dataset\\\\data_test.csv'\n",
    "data = pd.read_csv(discfile)\n",
    "data=data['number']\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x502bb38>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(figsize=(6,4))\n",
    "# print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#使用一阶差分，12步差分处理时间序列\n",
    "diff_1 = data.diff(1)\n",
    "diff1 = diff_1.dropna()\n",
    "diff1_144_1 = diff_1-diff_1.shift(144)\n",
    "diff1_144 = diff1_144_1.dropna()\n",
    "#print(diff1_144_1)\n",
    "#判断序列是否平稳，计算ACF，PACF\n",
    "fig1 = plt.figure(figsize=(12,8))\n",
    "ax1=fig1.add_subplot(111)\n",
    "sm.graphics.tsa.plot_acf(diff1_144,lags=40,ax=ax1)\n",
    "fig2 = plt.figure(figsize=(12,8))\n",
    "ax2=fig2.add_subplot(111)\n",
    "sm.graphics.tsa.plot_pacf(diff1_144,lags=40, ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\software\\develop\\python\\python35\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:221: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8758.668067654984 8795.263040465918 8773.116977761068\n"
     ]
    }
   ],
   "source": [
    "#模型定阶，根据aic,bic,hqic,三者都是越小越好\n",
    "# arma_mod01 = sm.tsa.ARMA(diff1_144,(0,1)).fit()\n",
    "# print(arma_mod01.aic,arma_mod01.bic,arma_mod01.hqic)\n",
    "# arma_mod10 = sm.tsa.ARMA(diff1_144,(1,0)).fit()\n",
    "# print(arma_mod10.aic,arma_mod10.bic,arma_mod10.hqic)\n",
    "# arma_mod60 = sm.tsa.ARMA(diff1_144,(6,0)).fit()\n",
    "# print(arma_mod60.aic,arma_mod60.bic,arma_mod60.hqic)\n",
    "arma_mod61 = sm.tsa.ARMA(diff1_144,(6,1)).fit()\n",
    "print(arma_mod61.aic,arma_mod61.bic,arma_mod61.hqic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0010192093347703\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#计算残差\n",
    "resid = arma_mod61.resid\n",
    "#看残差的acf和pacf,残差自相关图断尾，所以残差序列为白噪声\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "ax1 = fig.add_subplot(211)\n",
    "fig = sm.graphics.tsa.plot_acf(resid.values.squeeze(), lags=40, ax=ax1)\n",
    "ax2 = fig.add_subplot(212)\n",
    "fig = sm.graphics.tsa.plot_pacf(resid, lags=40, ax=ax2)\n",
    "\n",
    "print(sm.stats.durbin_watson(arma_mod61.resid.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 残差DW检验，DW的值越接近2，表示越不相关\n",
    "r,q,p = sm.tsa.acf(resid.values.squeeze(), qstat=True)\n",
    "d = np.c_[range(1,41), r[1:], q, p]\n",
    "table = pd.DataFrame(d, columns=['lag', \"AC\", \"Q\", \"Prob(>Q)\"])\n",
    "# print(table.set_index('lag'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# 用模型预测\n",
    "#predict_data=arma_mod61.predict(start='2017/04/04 23:50',end='2017/04/06 00:00',dynamic=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      597816.0\n",
      "1      583104.0\n",
      "2      572465.0\n",
      "3      561279.0\n",
      "4      551589.0\n",
      "5      546159.0\n",
      "6      533176.0\n",
      "7      530391.0\n",
      "8      522041.0\n",
      "9      512983.0\n",
      "10     506097.0\n",
      "11     500271.0\n",
      "12     494179.0\n",
      "13     487094.0\n",
      "14     480757.0\n",
      "15     474739.0\n",
      "16     470707.0\n",
      "17     466467.0\n",
      "18     462827.0\n",
      "19     458408.0\n",
      "20     454969.0\n",
      "21     450804.0\n",
      "22     448096.0\n",
      "23     446421.0\n",
      "24     444913.0\n",
      "25     442705.0\n",
      "26     440502.0\n",
      "27     439000.0\n",
      "28     438768.0\n",
      "29     438406.0\n",
      "         ...   \n",
      "691         NaN\n",
      "692         NaN\n",
      "693         NaN\n",
      "694         NaN\n",
      "695         NaN\n",
      "696         NaN\n",
      "697         NaN\n",
      "698         NaN\n",
      "699         NaN\n",
      "700         NaN\n",
      "701         NaN\n",
      "702         NaN\n",
      "703         NaN\n",
      "704         NaN\n",
      "705         NaN\n",
      "706         NaN\n",
      "707         NaN\n",
      "708         NaN\n",
      "709         NaN\n",
      "710         NaN\n",
      "711         NaN\n",
      "712         NaN\n",
      "713         NaN\n",
      "714         NaN\n",
      "715         NaN\n",
      "716         NaN\n",
      "717         NaN\n",
      "718         NaN\n",
      "719         NaN\n",
      "720         NaN\n",
      "Name: number, Length: 721, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\software\\develop\\python\\python35\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:221: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "  ' ignored when e.g. forecasting.', ValueWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8758.668067654984 8795.263040465918 8773.116977761068\n",
      "2.0010192093347703\n",
      "            AC          Q  Prob(>Q)\n",
      "lag                                \n",
      "1.0  -0.002061   0.001844  0.965745\n",
      "2.0  -0.002382   0.004313  0.997846\n",
      "3.0   0.002806   0.007746  0.999819\n",
      "4.0   0.009749   0.049282  0.999701\n",
      "5.0   0.027425   0.378778  0.995894\n",
      "6.0   0.029323   0.756344  0.993193\n",
      "7.0   0.029987   1.152130  0.991988\n",
      "8.0   0.009448   1.191510  0.996725\n",
      "9.0   0.019532   1.360218  0.998057\n",
      "10.0 -0.044771   2.248768  0.994053\n",
      "11.0 -0.040862   2.990675  0.990849\n",
      "12.0  0.011344   3.047987  0.995196\n",
      "13.0  0.056183   4.457255  0.985284\n",
      "14.0 -0.101288   9.048682  0.827919\n",
      "15.0  0.009780   9.091587  0.872680\n",
      "16.0 -0.136810  17.508518  0.353451\n",
      "17.0 -0.040942  18.264127  0.372364\n",
      "18.0  0.076638  20.918145  0.283563\n",
      "19.0 -0.032965  21.410374  0.314575\n",
      "20.0 -0.036031  21.999855  0.340518\n",
      "21.0 -0.054508  23.352253  0.325488\n",
      "22.0 -0.073111  25.791203  0.260755\n",
      "23.0 -0.095535  29.965991  0.150380\n",
      "24.0 -0.000842  29.966316  0.185871\n",
      "25.0 -0.017792  30.111820  0.220129\n",
      "26.0 -0.036573  30.728169  0.238549\n",
      "27.0 -0.020433  30.921030  0.274393\n",
      "28.0 -0.015075  31.026267  0.315915\n",
      "29.0 -0.007246  31.050639  0.363041\n",
      "30.0  0.028092  31.417917  0.395076\n",
      "31.0  0.014406  31.514743  0.440504\n",
      "32.0  0.034204  32.061932  0.463676\n",
      "33.0  0.065449  34.070501  0.415925\n",
      "34.0  0.002279  34.072943  0.464230\n",
      "35.0  0.038986  34.789224  0.478235\n",
      "36.0  0.011694  34.853837  0.522990\n",
      "37.0  0.023147  35.107616  0.558008\n",
      "38.0  0.009323  35.148894  0.602008\n",
      "39.0  0.015081  35.257169  0.641312\n",
      "40.0 -0.001944  35.258973  0.683390\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'The `start` argument could not be matched to a location related to the index of the data.'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: an integer is required",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32md:\\software\\develop\\python\\python35\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3077\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3078\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3079\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: '2017/4/4 23:50'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: an integer is required",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32md:\\software\\develop\\python\\python35\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py\u001b[0m in \u001b[0;36m_get_index_label_loc\u001b[1;34m(self, key, base_index)\u001b[0m\n\u001b[0;32m    415\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlong\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minteger\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 416\u001b[1;33m                     \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrow_labels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    417\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\software\\develop\\python\\python35\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3079\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3080\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3081\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: '2017/4/4 23:50'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32md:\\software\\develop\\python\\python35\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py\u001b[0m in \u001b[0;36m_get_prediction_index\u001b[1;34m(self, start, end, index, silent)\u001b[0m\n\u001b[0;32m    476\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 477\u001b[1;33m             \u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstart_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstart_oos\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_index_label_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    478\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\software\\develop\\python\\python35\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py\u001b[0m in \u001b[0;36m_get_index_label_loc\u001b[1;34m(self, key, base_index)\u001b[0m\n\u001b[0;32m    422\u001b[0m             \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 423\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    424\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex_was_expanded\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\software\\develop\\python\\python35\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py\u001b[0m in \u001b[0;36m_get_index_label_loc\u001b[1;34m(self, key, base_index)\u001b[0m\n\u001b[0;32m    411\u001b[0m             loc, index, index_was_expanded = (\n\u001b[1;32m--> 412\u001b[1;33m                 self._get_index_loc(key, base_index))\n\u001b[0m\u001b[0;32m    413\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\software\\develop\\python\\python35\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py\u001b[0m in \u001b[0;36m_get_index_loc\u001b[1;34m(self, key, base_index)\u001b[0m\n\u001b[0;32m    363\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mIndexError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 364\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    365\u001b[0m             \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"invalid literal for int() with base 10: '2017/4/4 23:50'\"",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-28-ab3d0e805dbf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     56\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m \u001b[1;31m# 用模型预测\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 58\u001b[1;33m \u001b[0mpredict_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0marma_mod61\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'2017/4/4 23:50'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'2017/4/6 00:00'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdynamic\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     59\u001b[0m \u001b[1;31m# print(predict_data)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     60\u001b[0m \u001b[1;31m# print(diff_1)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\software\\develop\\python\\python35\\lib\\site-packages\\statsmodels\\base\\wrapper.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     93\u001b[0m             \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrap_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     94\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mhow\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 95\u001b[1;33m             \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrap_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     96\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     97\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\software\\develop\\python\\python35\\lib\\site-packages\\statsmodels\\tsa\\arima_model.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, start, end, exog, dynamic)\u001b[0m\n\u001b[0;32m   1504\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1505\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdynamic\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1506\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdynamic\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1507\u001b[0m     \u001b[0mpredict\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_arma_results_predict\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1508\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\software\\develop\\python\\python35\\lib\\site-packages\\statsmodels\\tsa\\arima_model.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, params, start, end, exog, dynamic)\u001b[0m\n\u001b[0;32m    717\u001b[0m         \u001b[1;31m# will return an index of a date\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    718\u001b[0m         start, end, out_of_sample, _ = (\n\u001b[1;32m--> 719\u001b[1;33m             self._get_prediction_index(start, end, dynamic))\n\u001b[0m\u001b[0;32m    720\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    721\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mout_of_sample\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mexog\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mk_exog\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\software\\develop\\python\\python35\\lib\\site-packages\\statsmodels\\tsa\\arima_model.py\u001b[0m in \u001b[0;36m_get_prediction_index\u001b[1;34m(self, start, end, dynamic, index)\u001b[0m\n\u001b[0;32m    649\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    650\u001b[0m         start, end, out_of_sample, prediction_index = (\n\u001b[1;32m--> 651\u001b[1;33m             super(ARMA, self)._get_prediction_index(start, end, index))\n\u001b[0m\u001b[0;32m    652\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    653\u001b[0m         \u001b[1;31m# This replaces the _validate() call\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\software\\develop\\python\\python35\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py\u001b[0m in \u001b[0;36m_get_prediction_index\u001b[1;34m(self, start, end, index, silent)\u001b[0m\n\u001b[0;32m    477\u001b[0m             \u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstart_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstart_oos\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_index_label_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    478\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 479\u001b[1;33m             raise KeyError('The `start` argument could not be matched to a'\n\u001b[0m\u001b[0;32m    480\u001b[0m                            ' location related to the index of the data.')\n\u001b[0;32m    481\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mend\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'The `start` argument could not be matched to a location related to the index of the data.'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#-*- coding: utf-8 -*-\n",
    "\n",
    "from __future__ import print_function\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "\n",
    "#读取Excel数据\n",
    "discfile = '..\\\\dataset\\\\data_test.csv'\n",
    "data = pd.read_csv(discfile)\n",
    "data=data['number']\n",
    "data.head()\n",
    "\n",
    "data.plot(figsize=(12,8))\n",
    "print(data)\n",
    "\n",
    "#使用一阶差分，12步差分处理时间序列\n",
    "diff_1 = data.diff(1)\n",
    "diff1 = diff_1.dropna()\n",
    "diff1_144_1 = diff_1-diff_1.shift(144)\n",
    "diff1_144 = diff1_144_1.dropna()\n",
    "#print(diff1_144_1)\n",
    "#判断序列是否平稳，计算ACF，PACF\n",
    "fig1 = plt.figure(figsize=(12,8))\n",
    "ax1=fig1.add_subplot(111)\n",
    "sm.graphics.tsa.plot_acf(diff1_144,lags=40,ax=ax1)\n",
    "fig2 = plt.figure(figsize=(12,8))\n",
    "ax2=fig2.add_subplot(111)\n",
    "sm.graphics.tsa.plot_pacf(diff1_144,lags=40, ax=ax2)\n",
    "\n",
    "#模型定阶，根据aic,bic,hqic,三者都是越小越好\n",
    "# arma_mod01 = sm.tsa.ARMA(diff1_144,(0,1)).fit()\n",
    "# print(arma_mod01.aic,arma_mod01.bic,arma_mod01.hqic)\n",
    "# arma_mod10 = sm.tsa.ARMA(diff1_144,(1,0)).fit()\n",
    "# print(arma_mod10.aic,arma_mod10.bic,arma_mod10.hqic)\n",
    "# arma_mod60 = sm.tsa.ARMA(diff1_144,(6,0)).fit()\n",
    "# print(arma_mod60.aic,arma_mod60.bic,arma_mod60.hqic)\n",
    "arma_mod61 = sm.tsa.ARMA(diff1_144,(6,1)).fit()\n",
    "print(arma_mod61.aic,arma_mod61.bic,arma_mod61.hqic)\n",
    "#计算残差\n",
    "resid = arma_mod61.resid\n",
    "#看残差的acf和pacf,残差自相关图断尾，所以残差序列为白噪声\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "ax1 = fig.add_subplot(211)\n",
    "fig = sm.graphics.tsa.plot_acf(resid.values.squeeze(), lags=40, ax=ax1)\n",
    "ax2 = fig.add_subplot(212)\n",
    "fig = sm.graphics.tsa.plot_pacf(resid, lags=40, ax=ax2)\n",
    "\n",
    "print(sm.stats.durbin_watson(arma_mod61.resid.values))\n",
    "# 残差DW检验，DW的值越接近2，表示越不相关\n",
    "r,q,p = sm.tsa.acf(resid.values.squeeze(), qstat=True)\n",
    "d = np.c_[range(1,41), r[1:], q, p]\n",
    "table = pd.DataFrame(d, columns=['lag', \"AC\", \"Q\", \"Prob(>Q)\"])\n",
    "print(table.set_index('lag'))\n",
    "\n",
    "# 用模型预测\n",
    "predict_data=arma_mod61.predict('2017/4/4 23:50','2017/4/6 00:00',dynamic=False)\n",
    "# print(predict_data)\n",
    "# print(diff_1)\n",
    "# 由于是用差分后的值做的预测，因此需要把结果还原\n",
    "# 144步差分还原\n",
    "diff1_144_shift=diff_1.shift(144)\n",
    "# print('print diff1_144_shift')\n",
    "print(diff1_144_shift)\n",
    "diff_recover_144=predict_data.add(diff1_144_shift)\n",
    "# 一阶差分还原\n",
    "diff1_shift=data.shift(1)\n",
    "diff_recover_1=diff_recover_144.add(diff1_shift)\n",
    "diff_recover_1=diff_recover_1.dropna() # 最终还原的预测值\n",
    "print('预测值')\n",
    "print(diff_recover_1)\n",
    "\n",
    "# 实际值、预测值、差分预测值作图\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "ax = data.ix['2017-04-01':].plot(ax=ax)\n",
    "ax = diff_recover_1.plot(ax=ax)\n",
    "fig = arma_mod61.plot_predict('2017/4/2 23:50', '2017/4/6 00:00', dynamic=False, ax=ax, plot_insample=False)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
